Method for determining the change in position of an item of luggage in order to examine a suspect region in this item of luggage

ABSTRACT

The invention relates to a method for determining the change in position of an item of luggage in order to examine a suspect region in this item of luggage.

CROSS REFERENCE TO RELATED APPLICATION

This application claims benefit of priority under 35 U.S.C. §119 andincorporates by reference German Patent Application No. 103 46 269.4filed Oct. 6, 2003.

FIELD OF THE INVENTION

The invention relates to a method for determining the change in positionof an item of luggage in order to examine a suspect region in this itemof luggage.

BACKGROUND

Currently it is possible to analyze items of luggage completely forexplosives. The underlying analysis methods prove very reliable but alsolaborious. Such a laborious analysis can take place in airports only inso-called third-stage apparatuses. There, the quantity of items ofluggage processed is much smaller than with the first- and second-stageapparatuses. With these third-stage apparatuses a high detection rateand a low false alarm rate is required. In order to be able to use sucha third-stage apparatus as a second-stage apparatus also, the analysistime must be clearly reduced.

This problem has been solved to date by allowing no physical separationbetween a first-stage and a second-stage apparatus (see U.S. Pat. No.5,182,764) or preventing a physical movement or rotation of the item ofluggage (see WO 03/065077 A2). However this is very costly and hardlyfeasible in practice.

SUMMARY

The object of the invention is therefore to provide a method which onthe one hand has a high detection rate with simultaneously low falsealarm rate, but has a much shorter examination time compared with theknown methods with the abovementioned framework conditions.

The object is achieved by a method with the features of claim 1. Withthe method according to the invention the relative change in position ofthe item of luggage is calculated by comparing two pictures of the sameitem of luggage which have been recorded in different examinationsystems. Because the coordinates of a suspect region, which have beenobtained in the first examination system are known, it is possible inthe second examination system to examine in more detail only thissuspect region, the coordinates of which from the first examinationsystem have now been converted to the coordinates in the secondexamination system. As a result, the time spent analyzing once again allthe parts of the item of luggage that have already been identified as anon-suspect region is saved. Any imaging system is possible as first andsecond examination systems, provided the calculation of the angles ofrotation is to take place about the vertical and horizontal axes andalso the translation. These include both video images and transmissionimages, for example by means of X-ray radiation. How the suspect regionis obtained in the first examination system is not essential to theinvention, with the result that for the first examination system and thesecond examination system, apparatuses can be used that operate oncompletely different technological principles. For the secondexamination machine, a transmission system is preferred in respect ofthe examination of the suspect region, but the invention is by no meanslimited to this. It is equally possible to use e.g. magnetic resonancetomography. In addition, the two examination systems can also be farapart. The item of luggage to be examined can be carried by hand ortransported via a vehicle with the result that even a conveyor beltbetween them can be dispensed with. Consequently, there is an enormoussaving as a result of the method according to the invention in analysistime with a simultaneously high detection rate and low false alarm rate.

An advantageous development of the invention provides that an opticaland/or geometrical pre-processing of the first and/or secondtransmission image takes place before determination of the change inposition. By an optical pre-processing it is meant within the scope ofthis invention that the image information of a transmission image ismanipulated such that the function of the image registration is improvedin respect of accuracy and reliability. This can be achieved for exampleby carrying out a local averaging and median formation to reduce thenoise. In addition, non-linear scale filters (see G. Aubert & P.Komprobst: Mathematical Problems in Image Processing: PartialDifferential Equations and the Calculus of Variations. Springer, N.Y.,2002) can also be used. These filters reduce the image informationcontent inside image segments, but retain edges so that the position ofthe image segments does not change. As a result, perspective changes dueto different viewing angles in the two examination systems can becompensated. A further possibility is to use look-up tables, gammafilters or histogram filters, as a result of which identical absorptionsalso look the same within the transmission image, which is necessary inparticular if the two examination systems are constructed or operatedifferently. Local features, e.g. edges, points or massive objects, canalso be highlighted. Finally, by geometric pre-processing is meantwithin the framework of this application a geometric rectification. Thisis necessary whenever the two examination systems have differentgeometries. In such a case, different representations result even if theexamined item of luggage is in the same position. Optical pre-processingmakes possible a better basis for comparing the pictures of the item ofluggage of the first and second examination systems. This leads to asimpler determination of the first angle of rotation about the verticalaxis, the second angle of rotation about the horizontal axis and thetranslation.

A further advantageous development of the invention provides thatseveral sets of first and/or second angles of rotation are issued in thecase of ambiguities. Although it is necessary as a result to examineseveral suspect regions, as a rule only a few regions are left over,with the result that there is a clear reduction in the regions to beexamined in the item of luggage. At the same time the detection rate iskept high and the false alarm rate remains low. The two hypotheses usedare that the item of luggage has or has not been flipped, i.e. that itis lying on the same side or on its opposite side. Ambiguities occur ifthe method cannot decide clearly between these two hypotheses. Preferredis the input in each case of a probability value or a confidence value(a number or a vector of numbers which provides information about thereliability of a result) for the first and/or second angles of rotation.As a result, an appraisal of the suspect regions found is carried outand an examination can be carried out first of the region for which theangles with the highest probability value have been found. As a resultit is more probable that dangerous contents of the item of luggage willbe discovered more quickly. By a probability value is meant in thepresent application a value which provides information as to how highthe reliability of the determined values for translation and angles ofrotation is (which also indicates whether the item of luggage has beenflipped over or not). This probability value can be used to allow otherentities (both a person and a machine) to decide on the quality of theimage registration. For example, a threshold value is used which, if notreached, causes the whole item of luggage to be scanned once again inthe second examination system. A further advantageous development of theinvention provides that the change in position of the item of luggage isdetermined using global features, in particular correlation, “mutualinformation” (see description relating to FIG. 3) or radial dimensionalvariables. One of the two pictures of the item of luggage is rotateduntil it is most similar to the other picture. The point of rotationmust be defined in both pictures. Preferably the centre of gravity ofthe image of the item of luggage is used. I_(ij) is the intensity valueof the image at point (ij). The resultant coordinates of the centre ofgravity of the image (x_(g), y_(g)) are then:

${x_{g} = \frac{\sum\limits_{i,j}{x_{i}I_{i,j}}}{\sum\limits_{i,j}I_{i,j}}},{y_{g} = {\frac{\sum\limits_{i,j}{y_{i}I_{i,j}}}{\sum\limits_{i,j}I_{i,j}}.}}$

In addition to the use of correlation and “mutual information”, the useof radial dimensional variables is also possible. The picture is dividedinto N angle segments (the evaluation takes place in each case in anangle range between φ and φ+Δφ) which are appraised with differentvalues, e.g. statistical moments. The values of the Nth segments arethen compared with the values of the N+nth segment, n corresponding tothe angle increment. This measurement is preferably coupled with a scaleanalysis. The results on different linear scales, i.e. at differentresolutions, are compared and these findings are combined, as a resultof which a reduced calculation time is obtained. Thus the calculationtime is reduced by a factor of 4 if the resolution is halved, as fewerimage spots must be analyzed. Preferably different comparison values canalso be used and their result taken jointly into account. By acomparison value is meant within the framework of the application afunction which has the two pictures as input parameters and provides anumber or a vector. This issued value is then related to the differencebetween the two images. The simplest example of this is the differencebetween the two image spots. If this is small, the two pictures areidentical. By global features is meant within the framework of thisapplication that all image spots of the image are used for imageregistration. This differs from the local features given below which areused as a subset of all these image spots. The respective subset must bedetermined. One possibility for this is the detection of comers andedges.

A further advantageous development of the invention provides that thechange in position of the item of luggage is determined using localfeatures, in particular using “Random Sample Consensus” (RANSAC), robustestimation methods, Hough transformations or least-square methods.Suitable local features are sought in both pictures, e.g. corners,edges, lines, significant points or small, easily-identifiable objects(such as metal buttons) inside the item of luggage. These features areallocated to one another by ascertaining where a specific feature in onepicture is to be found in the second picture. As a result it is possibleto determine the information necessary for the transformation of thecoordinates of the suspect region—namely the first angle of rotationabout the vertical axis, the second angle of rotation about thehorizontal axis and the translation—by which the features can be changedinto one another. This method delivers more accurate results if themapping geometries of the two imaging systems are known. The question ofwhich of the preferred methods—RANSAC (see also on this: “Random SampleConsensus: A Paradigm for Model Fitting with Applications to ImageAnalysis and Automated Cartography” in Comm. of the ACM, Vol. 24, p.381-395, 1981), robust estimation methods, Hough transformations orleast-square methods—is used depends on the calculation speed and thequality of the allocation of the features. For the allocation of thefeatures, reference can be made to the same values as for global imageinformation.

Particularly preferably the determination of the change in position ofthe item of luggage is carried out using a coupling of the analysis bymeans of global features with an analysis by means of local features.Such a coupling can take place for example by using both imageregistration methods and forming a weighted average of the two results.This weighted average can then serve as a function of the probabilityvalue. Another coupling can also take place by using the local featuresonly when the probability value of the global features is not highenough. As a result particularly reliable and rapidly obtainableinformation on the transformation of the coordinates is obtained.

A further advantageous development of the invention provides that thedetermination of the change in position using local features takes placeon different linear scales. For example, this can take place in that thecalculation is carried out on one linear scale and the results comparedwith a comparable analysis on another linear scale. Preferably, thelocal features are chosen according to the linear scale. On each linearscale the features are chosen which can best be measured there. Thisleads to a simplification of the determination of the change in positionof the item of luggage. For example, the original image of the pictureis used as linear scale 1. Linear scale 2 then corresponds to theoriginal image with lower resolution (see on this point alsoSkalenpyramide [Scale Pyramids] in Jäahne, Digitale Bildverarbeitung[Digital Image Processing], Springer 1997). By a scale analysis is meantwithin the framework of this application that the resolution of thepictures is successively increased. Image registration is then carriedout at each resolution stage. Firstly, the image registration of acoarse resolution begins, for example the linear scale 4. This producesa position with a confidence interval. Then the resolution is increased,for example to the linear scale 3, and the image registration isoperated only in the confidence interval. These steps are then carriedout up to maximum resolution (original image of the linear scale 1). Theadvantage comprises on the one hand the reduced calculation time and onthe other hand the robustness of this method vis-à-vis geometricdistortions which can lead to incorrect registrations, in particular athigher resolutions.

A further advantageous development of the invention provides that onlythe local features are used which do not contradict the analysis usingthe global features. As a result the allocation of the local features isimproved.

Further advantageous developments of the invention are the subject ofthe dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantageous designs of the invention are explained in more detail withreference to the drawings. Individually there are shown in:

FIG. 1 a flowchart of a method for comparing the pictures of an item ofluggage and for determining the change in position of the item ofluggage,

FIGS. 2 a-c representations of the different pre-processing steps usingan item of luggage,

FIGS. 3 a-d four stages with different dimensional scales for carryingout the scale analysis,

FIG. 4 diagram of a determination of position by means of “mutualinformation”,

FIG. 5 diagram of a determination of position by means of scale analysisand image comparison by correlation,

FIGS. 6 a-d example of a features extraction by means of local featureswith reference to two representations of an item of luggage in differentarrangement with correlated sections in each case and

FIG. 7 example of a successful image match on the basis of the methodaccording to the invention with an item of luggage in two differentpositions.

FIG. 8 is a perspective view of a conveyer belt and a coordinate systemaccording to an embodiment of the present invention, showing a firstangle of rotation about a vertical axis, a second angle of rotationabout a horizontal axis, and a translation.

DETAILED DESCRIPTION

FIG. 1 shows a flowchart with a schematic representation of a method forcomparing the picture of an item of luggage 4 and for determining thechange in position of the item of luggage 4. A common coordinates systemis agreed for the two examination systems. As shown in FIG. 8, areference system geared to the conveyor belt 200 has proved to bepracticable. It is a Cartesian coordinates system the X-axis 202 ofwhich runs transverse to the direction of transport 204 and the zeropoint of which lies on the edge of the conveyor belt. The Y-coordinate206 points against the direction of transport and begins at the edge ofthe suitcase. The Z-coordinate 208 begins on the conveyor belt andpoints upwards. A right-handed coordinates system thus results. In theembodiment shown only a single angle of rotation 210 is given whichrelates to a rotation of the item of luggage 4 about the Z-axis 208. Thesecond angle of rotation 212 usually required is replaced by informationwhether the item of luggage 4 has been flipped or not. By “flip” ismeant here a rotation of the item of luggage by 180° about the X-axis.To be able to carry out a clear transfer from the first examinationsystem into the second examination system, translation 214 must still bedefined. It is the movement of the item of luggage 4 on the conveyorbelt in the X-Y plane.

In a first-stage apparatus 100, a first picture or first transmissionimage 1 is taken. The item of luggage 4 (see FIGS. 2, 3, 6 and 7) is ina position determined by the first transmission image 1. In a secondtransmission unit 102 a second picture or second transmission image 2 ofthe same item of luggage 4 is taken in a second position which usuallydeviates from the first position. Firstly both transmission images 1, 2are each subjected to a pre-processing 10, 20. Both a geometricrectification and an optical pre-processing of the intensities arecarried out. More precise details are explained below with reference toFIGS. 2 a-c. Different features of the respective image contents arethen measured to be able to undertake a features extraction 11, 21.Comparative features are also determined. More precise details on thefeatures extraction 11, 21 and the resulting determination of positionthrough the establishment of the change in position of the comparativefeatures are detailed below with reference to FIGS. 3 a-d, 4, 5 and 6a-d (FIGS. 4, 5 and 6 a-d being provided with arbitrary X- and Y-scalevalues). The extracted features are appraised. With the help of thesuitable features, a calculation 30 of the change in position is carriedout. The method according to the invention works particularly well if ithas the possibility of appraising a made angle estimation and issuingone or more angles of rotation plus an angular measure. As a result theinclusion of errors, caused by an incorrect angle determination, in thesubsequent analysis is prevented. There is then also a geometrictransformation 31 of the images. In addition to the image of the firststage of the first-stage apparatus, a first list 12 of coordinates ofthe first suspect regions 13 is provided as well. Following thesuccessful determination of the position via the calculation 30 of thechange in position, a second list 22 with transformed second suspectregions 23 is calculated and issued, which then relates to the secondtransmission image 2.

Three phases in respect of the pre-processing 10 of a first transmissionimage 1, also representing the second transmission image 2, are shown inFIG. 2. The type of pre-processing 10, 20 depends on the transmissionapparatuses used and their mapping geometry. In the example given here,it is a detector with L-shaped geometry. The originally recordedtransmission image 1, 2 is shown in FIG. 2 a. In a first pre-processingstep an optical calibration is carried out in which the full dynamicrange of the intensity values is used, a so-called histogram adaptation(FIG. 2 b).

The result of the representation of the item of luggage 4 after carryingout a second step can be seen in FIG. 2 c. The distorted image has beenrectified. This is possible without problems if the geometry of thetransmission unit, in particular the arrangement of the X-ray tubesrelative to the detector and also the relative position of the objectrelative to both and the geometry of the detector are known.

The two steps mentioned above together serve to place images fromdifferent transmission apparatuses onto a common, comparable basis.

The four lowermost steps of a scale pyramid are shown in FIGS. 3 a-d.These are used in the features extraction 11, 21. The resolutionincreases gradually from FIG. 3 a to FIG. 3 d. In addition to the use offour stages of the scale pyramid, any other number of stages is alsopossible.

The use of firstly a scale analysis and secondly an image comparison bymeans of correlation and “mutual information” is described here as anexample of features extraction. Following the pre-processing 10, 20 ofthe transmission images 1, 2, these are subjected to scale analysis.This means that the correlation

${C\left( {{I_{1}\left( {r,\phi} \right)},{I_{2}\left( {r,{\phi + {\Delta\;\phi}}} \right)}} \right)} = {\sum\limits_{r,\phi}{\left( {{I_{1}\left( {r,\phi} \right)} - {{\overset{\_}{I}}_{1}\left( {r,\phi} \right)}} \right)\left( {{I_{2}\left( {r,\phi} \right)} - {{\overset{\_}{I}}_{2}\left( {r,\phi} \right)}} \right)}}$is calculated here on different spatial resolutions or linear scales. I₁and I₂ correspond to the projections of the transmission images onto theconveyor belt. For simplicity's sake, the Cartesian coordinates havebeen transformed into polar coordinates. A projection onto other planesis equally possible. The image on the coarsest linear scale (see FIG. 3a) consists of only approx. 40×40 image spots. In these images, thetopology changes which always occur with rotated objects in transmissionimages play a diminished role. The further analysis initially takesplace using only the coarsest linear scale. It is then graduallyextended also to include finer linear scales with higher resolution. Onthe lowest plane of the first transmission image 1, which is usuallycalled first-stage image, this is compared with a prescanner image ofthe second transmission image 2 by means of a correlation method fordifferent angles of rotation. This is also carried out with a suitcaseflipped by 180°. In the present example the abovementioned value C(I₁(r,φ), I₂(r, φ+Δφ)) is used as a measure of correlation between thestandardized transmission images 1, 2. Ī_(1,2) are the averageintensities of the image, I_(1,2)(r,φ) the intensity value for r and φ.Alternatively, “mutual information” can also be used. In this case thethree probability densities p(a), p(b) and p(a,b) are calculated. p(a)and p(b) are the probability densities of specific amplitude values,p(a,b) the probability density that a pixel simultaneously has a value aand a value b. The “mutual information” of these three probabilitydensities is compared. The result is:I=H(p(a))+H(p(b))−H(p(a,b))

H stands for entropy. This is defined from:

H(p(x₁, …  , x_(n))) = ∫_(−∞)^(∞)𝕕x₁  …  ∫_(−∞)^(∞)𝕕x_(n)p(x₁, …  , x_(n))  log (p(x₁, …  , x_(n))).

The calculation of the correlation or the “mutual information”corresponds to the step of features extraction 11, 21 as well as inparts the calculation of the change in position 30. With a determinationof position using global features, the image is also rotated about itscentre of gravity on the lowest variables scale.

In FIG. 4 the pattern of the values for various angles of rotation isshown. The image with the coarsest linear scale of the secondtransmission image 2 has been rotated about its centre of gravity andcompared with the image of the first-stage apparatus, i.e. the firsttransmission image I with regard to its centre of gravity. The valueanalysis on the coarsest linear scale delivers different maximum points.These are used in the next step to determine the values more accuratelyon the higher planes. This means that, instead of tuning all the anglesof rotation on every plane, only the best candidates are used on thenext-highest plane.

At the end of this analysis, which has been carried out on all theplanes of the scale pyramid, the correlation results are analyzed andthe angle of rotation of the item of luggage 4 determined. Other datacan be and are also still taken into account. With the help of theinstantaneous analysis of the image, the angles of rotation of the twotransmission images 1, 2 have been calculated and compared with theresult of the method described above. In addition, the maxima of thedifferent values of the flipped or unflipped item of luggage 4 have beencompared with each other and evaluated. Where the analysis cannotdetermine an unambiguous angle, further angles are then issued ifnecessary. A further refinement is possible using local features (seebelow re FIGS. 6 a-d). As the change in position of the item of luggage4 is known, the new position of the suspect region can be estimated. Afinal scan can now be carried out in this region. The lines shown inFIG. 4 belong in one case to a non-flipped suitcase (line 1) and in theother case to a flipped suitcase (line 2).

FIG. 5 shows, instead of the “mutual information” of FIG. 4, thecorrelation for different angular measures of the first transmissionimage I with the second transmission image 2 on the coarsest linearscale. Two lines are shown here also, one line (line 1) belonging to anon-flipped suitcase and the second line (line 2) corresponding to aflipped suitcase.

FIGS. 6 a-d show the method of features extraction 11, 21 using localfeatures. FIGS. 6 a and 6 c show the same item of luggage 4 in differentpositions. FIG. 6 b shows a first image section 13 from FIG. 6 a inwhich a bottle can be seen. The same bottle has been found in FIG. 6 dafter implementing the method according to the invention.

The determination of position by means of local features can be carriedout for itself alone or as a postprocessing step to a positiondetermination using the global features (as carried out above for FIGS.3 a-d). The preprocessing 10, 20 for this has been carried out inadvance. Further steps may possibly still be necessary, depending on howthe local features are defined. Various first image sections 13 withlocal features are firstly ascertained from the first transmission image1. This can be achieved for example by determining edges, comers orregions of high intensity. In addition, the entropy of the localamplitude statistics can also be measured. The amplitude statisticsdescribe the probability density inside the first image section 13 xε[x₀,x₀+L_(x)], y ε[y₀,y₀+L_(y)] of obtaining a specific amplitude valueĪ. It is calculated from:

${p_{x_{0},y_{0}}\left( \overset{\sim}{I} \right)} = {\sum\limits_{x = x_{0}}^{L_{x}}{\sum\limits_{y = y_{0}}^{L_{y}}{{\delta\left( {{I\left( {x,y} \right)} - \overset{\sim}{I}} \right)}.}}}$L_(x), L_(y) describe the size of the first image section 13 and x₀, y₀the position of the first image section 13 in the image. δ represents adelta function. This is one if the intensity of the first image section13 corresponds to the value Ī.

A characteristic of the amplitude statistics is that they contain nospatial information. They are therefore independent of the relativeposition of individual objects of the first image section 13.

Analogously, in the second transmission image 2 local features arelikewise sought according to the same or sensibly broadened rules, whichcorresponds to the second features extraction 21 shown in FIG. 1. Forboth transmission images 1, 2 there is a set of features with theircoordinates {X_(1,2) (x, y)}. In the next step an attempt is made toallocate the different features to one another. Alternatively the searchfor features in an image can be dispensed with. The features of theother transmission image 1, 2 are then sought in the complete image.

By way of example, the features are described below via their amplitudestatistics. It is assumed that the two image sections 13, 23 of FIGS. 6b and 6 d which are the most similar contain the sought feature. In themethod shown here, the difference as regards the different moments ofthe amplitude statistics and the entropy has been used as a measure ofsimilarity. Other measures of similarity are for example the value ofthe convolution integral of the probability densities.

A first image section 13 (FIG. 6 b) has been chosen from the firsttransmission image 1 of the item of luggage 4 (FIG. 6 a). A second imagesection 23 with the same dimensions has been sought in the secondtransmission image 2 (FIG. 6 c) which resembles the first image section13 in its statistical properties. As the second image section 23 of FIG.6 b is a rectangle, but the position of the item of luggage 4 haschanged between FIG. 6 a and FIG. 6 c through a rotation, not all of thebottle is mapped in FIG. 6 d. This problem could be overcome for exampleby using not rectangular, but circular, image sections 13, 23.

As a result, some of the different features were able to be allocated toone another. The pairs thus describe two views of the same feature. Ifit is assumed the only the item of luggage 4, but not its contents, haschanged position, it is possible to calculate the change in position 30of the item of luggage 4 using the new coordinates.

It is true for each feature x_(i) that it satisfies the valid mappingequation (see also Richard Hartley and Andrew Zisserman in “MultipleView Geometry in Computer Vision”; Second Edition; Cambridge UniversityPress, March 2004), which reads:0=x′ _(i) ^(T) Fx _(i).F represents the fundamental matrix of the imaging system, x′ the mappedspot and x the coordinates of the real spot. The relationship of thepair of features is thus the following:0=x′ _(i) ^(T) F ₁ x _(i)0=x″ _(i) ^(T) F ₂ T _(ψ,φ,x,y) x _(i)T_(ψ,φ,x,y) describes the change in position of the feature(translations and rotations); x″ stands for the image coordinates in thesecond transmission image 2.

There is a range of different techniques for the solution of thismathematical problem. If it is guaranteed that the allocations of thefeatures is good enough, an attempt can be made using a least-squaremethod to determine the sought angles of rotation and translations.Otherwise, so-called robust estimation methods can be used.

The principle of the invention which has been described in detail abovewith reference to FIGS. 1 to 6 can be summarized as follows withreference to FIG. 7:

A first transmission image 1 of an item of luggage 4 from a first-stageapparatus 100 (left-hand side) is compared 104 with a secondtransmission image 2 of the same item of luggage 4 of a furthertransmission apparatus 102. The item of luggage 4 has been both rotatedand flipped. On the basis of the implemented method according to theinvention, an allocation of the first suspect region 13 (which is drawnin as a rectangle for clarity) to the second suspect region 23 is quiteeasily achieved.

To greatly reduce the analysis time, in a second-stage apparatus 102 afurther analysis is carried out only of the second suspect region 23which has been classified as suspect in a first transmission image 1 ina first-stage apparatus 100. The coordinates of the first suspect region13 ascertained by the first-stage apparatus and a line-scan image aretransmitted to the second-stage apparatus. As both apparatuses arephysically separated from each other and the item of luggage 4 is thususually brought via different transport systems from the first-stageapparatus to the second-stage apparatus, the coordinates must be adaptedto the new position of the item of luggage 4. For this the second-stageapparatus is also equipped with a line scanner. After the scanning ofthe second transmission image 2, the two transmission images 1, 2 areeach subjected to a pre-processing 10, 20 by means of a calibration withthe result that they can be compared 104 to one another. This isfollowed in each case by a features extraction 11, 21 by means of globaland/or local features for each of the two transmission images 1, 2. Onthe basis of the features obtained from the two features extractions 11,21, the change in position of the object 4 can be calculated ordetermined 106 by means of a comparison 104. It is thereby possible thatonly the second suspect region 23 is also analyzed in the second-stageapparatus, and no longer the whole object 4. The method according to theinvention thus saves a great deal of time during the analysis of theobject 4 without the detection rate falling or the false alarm rateincreasing.

1. A method for determining the change in position of an item of luggagein order to examine a suspect region in this item of luggage, the methodcomprising: taking a first picture of the item of luggage by a firstexamination system; conveying the item of luggage from the firstexamination system to a second examination system which is physicallyseparated from the first examination system; transferring the firstpicture of the item of luggage and of coordinates of a first suspectregion in the item of luggage from the first examination system to thesecond examination system; taking a second picture of the item ofluggage (4) by the second examination system; comparing the two picturesof the item of luggage of the first and second examination systems;determining the change in position of the item of luggage giving a firstangle of rotation about the vertical axis, a second angle of rotationabout the horizontal axis and a translation; determining the coordinatesof the second suspect region which corresponds to the first suspectregion in the first examination system in the second examination system;and performing targeted examination of the item of luggage only in thearea of the coordinates of the second suspect region in the secondexamination system.
 2. The method according to claim 1, wherein apicture of a first transmission image is taken in the first examinationsystem and a first suspect region in the item of luggage is determinedby a first transmission apparatus.
 3. The method according to claim 2,wherein an optical or geometrical pre-processing of the first or secondtransmission image takes place before the determination of the change inposition.
 4. The method according to claim 1, wherein several sets offirst or second angles of rotation are issued in the case ofambiguities.
 5. The method according to claim 4, wherein a probabilityvalue is given in each case for the first or second angles of rotation.6. The method according to claim 1, wherein the change in position ofthe item of luggage is determined using global features, in particularcorrelation, mutual information or radial dimensional variables.
 7. Themethod according to claim 6, wherein determining the change in positionof the item of luggage and/or determining the coordinates includes ascale analysis.
 8. The method according to claim 6, wherein variouscomparison values are used.
 9. The method according to claim 1, whereinthe change in position of the item of luggage is determined using localfeatures, including RANSAC, robust estimation methods, Houghtransformations or least-square methods.
 10. The method according toclaim 9, wherein determining the change in position of the item ofluggage and/or determining the coordinates includes a scale analysis.11. The method according to claim 9, wherein the determination of thechange in position using local features takes place on different linearscales.
 12. The method according to claim 11, wherein the local featuresare chosen according to the linear scale.
 13. The method according toclaim 1, wherein a comparison takes place of the results of the analysisusing the global features with the results of the analysis using thelocal features.
 14. The method according to claim 1, wherein only thelocal features are used which do not contradict the analysis using theglobal features.